Vector Embeddings in Large Language Models

Vector embeddings are a powerful tool in natural language processing (NLP) that allows us to represent words, phrases, and even entire documents as vectors of numbers. These vectors can then be used in a variety of NLP tasks, such as sentiment analysis, machine translation, and text classification. In this blog post, we will explore vector embeddings in the context of large language models (LLMs), which are a type of neural network that have revolutionized NLP in recent years. We will cover the basics of vector embeddings, including how they are created and how they can be used in LLMs. We will also provide technical details, equations, and code examples where necessary.

What are Vector Embeddings?

Vector embeddings are lists of numbers that represent some kind of data, such as words, phrases, or images. In the context of NLP, vector embeddings are used to represent words and phrases as vectors of numbers. The idea behind vector embeddings is to capture the meaning of a word or phrase in a way that can be easily processed by a computer. This is done by mapping each word or phrase to a vector in a high-dimensional space, where similar words or phrases are mapped to nearby vectors.

One of the most popular methods for creating vector embeddings is Word2Vec. Word2Vec is a neural network that takes a large corpus of text as input and learns to map each word to a vector in a high-dimensional space. The resulting vectors capture the meaning of each word in the context of the…